Visualizing Data for Impact: Analyzing Misleading Visualizations

Data Visualization    |    Intermediate
  • 8 videos | 27m 1s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 30 users Rating 4.6 of 30 users (30)
One of the challenges of data visualization is recognizing and avoiding misleading visuals. These and other common mistakes make data visualization less effective and can lead to incorrect conclusions. Through this course, learn about misleading statistics and visual distortions. Examine some common data visualization mistakes, including data overload, interchanging charts, and the use of color, as well as how to recognize and correct them. Next, explore examples of deceiving statistics, visual distortions, and graphs and how to avoid being misleading. Finally, learn about omitting data, improper extraction, and correlating causation. After course completion, you'll be able to avoid mistakes when visualizing your data.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    List and address common visualization mistakes, such as data overload, interchanging charts, and scaling
    Recognize common visualization mistakes, such as the use of color and using wrong charts
    Name examples of misleading statistics and how to avoid being misleading
  • Outline visual distortions, such as truncated graphs, exaggerated scaling, and ignored conventions
    Identify visualizations with numbers that don’t add up and 3d distortions
    Recognize details about omitting data, improper extraction, and correlating causation
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 48s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 4m 33s
    Upon completion of this video, you will be able to list and address common visualization mistakes, such as data overload, interchanging charts, and scaling. FREE ACCESS
  • Locked
    3.  Issues with Color and Chart Selection
    3m 32s
    After completing this video, you will be able to recognize common visualization mistakes, such as the use of color and using wrong charts. FREE ACCESS
  • Locked
    4.  Misleading Statistics
    4m 20s
    In this video, we will name examples of misleading statistics and how to avoid being misleading. FREE ACCESS
  • Locked
    5.  Visual Distortions
    4m 29s
    Upon completion of this video, you will be able to outline visual distortions, such as truncated graphs, exaggerated scaling, and ignored conventions. FREE ACCESS
  • Locked
    6.  Deceiving Graphs
    3m 46s
    In this video, we will identify visualizations with numbers that don’t add up and 3D distortions. FREE ACCESS
  • Locked
    7.  Data Omission and Misleading Visualizations
    5m 1s
    After completing this video, you will be able to recognize details about omitting data, improper extraction, and correlating causation. FREE ACCESS
  • Locked
    8.  Course Summary
    33s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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